/* ----------------------------------------------------------------------------
* Copyright (C) 2010 ARM Limited. All rights reserved.
*
* $Date:        15. February 2012
* $Revision: 	V1.1.0
*
* Project: 	    CMSIS DSP Library
* Title:		arm_conv_f32.c
*
* Description:	Convolution of floating-point sequences.
*
* Target Processor: Cortex-M4/Cortex-M3/Cortex-M0
*
* Version 1.1.0 2012/02/15
*    Updated with more optimizations, bug fixes and minor API changes.
*
* Version 1.0.11 2011/10/18
*    Bug Fix in conv, correlation, partial convolution.
*
* Version 1.0.10 2011/7/15
*    Big Endian support added and Merged M0 and M3/M4 Source code.
*
* Version 1.0.3 2010/11/29
*    Re-organized the CMSIS folders and updated documentation.
*
* Version 1.0.2 2010/11/11
*    Documentation updated.
*
* Version 1.0.1 2010/10/05
*    Production release and review comments incorporated.
*
* Version 1.0.0 2010/09/20
*    Production release and review comments incorporated
*
* Version 0.0.7  2010/06/10
*    Misra-C changes done
*
* -------------------------------------------------------------------------- */

#include "arm_math.h"

/**
 * @ingroup groupFilters
 */

/**
 * @defgroup Conv Convolution
 *
 * Convolution is a mathematical operation that operates on two finite length vectors to generate a finite length output vector.
 * Convolution is similar to correlation and is frequently used in filtering and data analysis.
 * The CMSIS DSP library contains functions for convolving Q7, Q15, Q31, and floating-point data types.
 * The library also provides fast versions of the Q15 and Q31 functions on Cortex-M4 and Cortex-M3.
 *
 * \par Algorithm
 * Let <code>a[n]</code> and <code>b[n]</code> be sequences of length <code>srcALen</code> and <code>srcBLen</code> samples respectively.
 * Then the convolution
 *
 * <pre>
 *                   c[n] = a[n] * b[n]
 * </pre>
 *
 * \par
 * is defined as
 * \image html ConvolutionEquation.gif
 * \par
 * Note that <code>c[n]</code> is of length <code>srcALen + srcBLen - 1</code> and is defined over the interval <code>n=0, 1, 2, ..., srcALen + srcBLen - 2</code>.
 * <code>pSrcA</code> points to the first input vector of length <code>srcALen</code> and
 * <code>pSrcB</code> points to the second input vector of length <code>srcBLen</code>.
 * The output result is written to <code>pDst</code> and the calling function must allocate <code>srcALen+srcBLen-1</code> words for the result.
 *
 * \par
 * Conceptually, when two signals <code>a[n]</code> and <code>b[n]</code> are convolved,
 * the signal <code>b[n]</code> slides over <code>a[n]</code>.
 * For each offset \c n, the overlapping portions of a[n] and b[n] are multiplied and summed together.
 *
 * \par
 * Note that convolution is a commutative operation:
 *
 * <pre>
 *                   a[n] * b[n] = b[n] * a[n].
 * </pre>
 *
 * \par
 * This means that switching the A and B arguments to the convolution functions has no effect.
 *
 * <b>Fixed-Point Behavior</b>
 *
 * \par
 * Convolution requires summing up a large number of intermediate products.
 * As such, the Q7, Q15, and Q31 functions run a risk of overflow and saturation.
 * Refer to the function specific documentation below for further details of the particular algorithm used.
 *
 *
 * <b>Fast Versions</b>
 *
 * \par
 * Fast versions are supported for Q31 and Q15.  Cycles for Fast versions are less compared to Q31 and Q15 of conv and the design requires
 * the input signals should be scaled down to avoid intermediate overflows.
 *
 *
 * <b>Opt Versions</b>
 *
 * \par
 * Opt versions are supported for Q15 and Q7.  Design uses internal scratch buffer for getting good optimisation.
 * These versions are optimised in cycles and consumes more memory(Scratch memory) compared to Q15 and Q7 versions
 */

/**
 * @addtogroup Conv
 * @{
 */

/**
 * @brief Convolution of floating-point sequences.
 * @param[in] *pSrcA points to the first input sequence.
 * @param[in] srcALen length of the first input sequence.
 * @param[in] *pSrcB points to the second input sequence.
 * @param[in] srcBLen length of the second input sequence.
 * @param[out] *pDst points to the location where the output result is written.  Length srcALen+srcBLen-1.
 * @return none.
 */

void arm_conv_f32(
    float32_t* pSrcA,
    uint32_t srcALen,
    float32_t* pSrcB,
    uint32_t srcBLen,
    float32_t* pDst)
{


#ifndef ARM_MATH_CM0

	/* Run the below code for Cortex-M4 and Cortex-M3 */

	float32_t* pIn1;                               /* inputA pointer */
	float32_t* pIn2;                               /* inputB pointer */
	float32_t* pOut = pDst;                        /* output pointer */
	float32_t* px;                                 /* Intermediate inputA pointer */
	float32_t* py;                                 /* Intermediate inputB pointer */
	float32_t* pSrc1, *pSrc2;                      /* Intermediate pointers */
	float32_t sum, acc0, acc1, acc2, acc3;         /* Accumulator */
	float32_t x0, x1, x2, x3, c0;                  /* Temporary variables to hold state and coefficient values */
	uint32_t j, k, count, blkCnt, blockSize1, blockSize2, blockSize3;     /* loop counters */

	/* The algorithm implementation is based on the lengths of the inputs. */
	/* srcB is always made to slide across srcA. */
	/* So srcBLen is always considered as shorter or equal to srcALen */
	if(srcALen >= srcBLen) {
		/* Initialization of inputA pointer */
		pIn1 = pSrcA;

		/* Initialization of inputB pointer */
		pIn2 = pSrcB;
	} else {
		/* Initialization of inputA pointer */
		pIn1 = pSrcB;

		/* Initialization of inputB pointer */
		pIn2 = pSrcA;

		/* srcBLen is always considered as shorter or equal to srcALen */
		j = srcBLen;
		srcBLen = srcALen;
		srcALen = j;
	}

	/* conv(x,y) at n = x[n] * y[0] + x[n-1] * y[1] + x[n-2] * y[2] + ...+ x[n-N+1] * y[N -1] */
	/* The function is internally
	 * divided into three stages according to the number of multiplications that has to be
	 * taken place between inputA samples and inputB samples. In the first stage of the
	 * algorithm, the multiplications increase by one for every iteration.
	 * In the second stage of the algorithm, srcBLen number of multiplications are done.
	 * In the third stage of the algorithm, the multiplications decrease by one
	 * for every iteration. */

	/* The algorithm is implemented in three stages.
	   The loop counters of each stage is initiated here. */
	blockSize1 = srcBLen - 1u;
	blockSize2 = srcALen - (srcBLen - 1u);
	blockSize3 = blockSize1;

	/* --------------------------
	 * initializations of stage1
	 * -------------------------*/

	/* sum = x[0] * y[0]
	 * sum = x[0] * y[1] + x[1] * y[0]
	 * ....
	 * sum = x[0] * y[srcBlen - 1] + x[1] * y[srcBlen - 2] +...+ x[srcBLen - 1] * y[0]
	 */

	/* In this stage the MAC operations are increased by 1 for every iteration.
	   The count variable holds the number of MAC operations performed */
	count = 1u;

	/* Working pointer of inputA */
	px = pIn1;

	/* Working pointer of inputB */
	py = pIn2;


	/* ------------------------
	 * Stage1 process
	 * ----------------------*/

	/* The first stage starts here */
	while(blockSize1 > 0u) {
		/* Accumulator is made zero for every iteration */
		sum = 0.0f;

		/* Apply loop unrolling and compute 4 MACs simultaneously. */
		k = count >> 2u;

		/* First part of the processing with loop unrolling.  Compute 4 MACs at a time.
		 ** a second loop below computes MACs for the remaining 1 to 3 samples. */
		while(k > 0u) {
			/* x[0] * y[srcBLen - 1] */
			sum += *px++ * *py--;

			/* x[1] * y[srcBLen - 2] */
			sum += *px++ * *py--;

			/* x[2] * y[srcBLen - 3] */
			sum += *px++ * *py--;

			/* x[3] * y[srcBLen - 4] */
			sum += *px++ * *py--;

			/* Decrement the loop counter */
			k--;
		}

		/* If the count is not a multiple of 4, compute any remaining MACs here.
		 ** No loop unrolling is used. */
		k = count % 0x4u;

		while(k > 0u) {
			/* Perform the multiply-accumulate */
			sum += *px++ * *py--;

			/* Decrement the loop counter */
			k--;
		}

		/* Store the result in the accumulator in the destination buffer. */
		*pOut++ = sum;

		/* Update the inputA and inputB pointers for next MAC calculation */
		py = pIn2 + count;
		px = pIn1;

		/* Increment the MAC count */
		count++;

		/* Decrement the loop counter */
		blockSize1--;
	}

	/* --------------------------
	 * Initializations of stage2
	 * ------------------------*/

	/* sum = x[0] * y[srcBLen-1] + x[1] * y[srcBLen-2] +...+ x[srcBLen-1] * y[0]
	 * sum = x[1] * y[srcBLen-1] + x[2] * y[srcBLen-2] +...+ x[srcBLen] * y[0]
	 * ....
	 * sum = x[srcALen-srcBLen-2] * y[srcBLen-1] + x[srcALen] * y[srcBLen-2] +...+ x[srcALen-1] * y[0]
	 */

	/* Working pointer of inputA */
	px = pIn1;

	/* Working pointer of inputB */
	pSrc2 = pIn2 + (srcBLen - 1u);
	py = pSrc2;

	/* count is index by which the pointer pIn1 to be incremented */
	count = 0u;

	/* -------------------
	 * Stage2 process
	 * ------------------*/

	/* Stage2 depends on srcBLen as in this stage srcBLen number of MACS are performed.
	 * So, to loop unroll over blockSize2,
	 * srcBLen should be greater than or equal to 4 */
	if(srcBLen >= 4u) {
		/* Loop unroll over blockSize2, by 4 */
		blkCnt = blockSize2 >> 2u;

		while(blkCnt > 0u) {
			/* Set all accumulators to zero */
			acc0 = 0.0f;
			acc1 = 0.0f;
			acc2 = 0.0f;
			acc3 = 0.0f;

			/* read x[0], x[1], x[2] samples */
			x0 = *(px++);
			x1 = *(px++);
			x2 = *(px++);

			/* Apply loop unrolling and compute 4 MACs simultaneously. */
			k = srcBLen >> 2u;

			/* First part of the processing with loop unrolling.  Compute 4 MACs at a time.
			 ** a second loop below computes MACs for the remaining 1 to 3 samples. */
			do {
				/* Read y[srcBLen - 1] sample */
				c0 = *(py--);

				/* Read x[3] sample */
				x3 = *(px);

				/* Perform the multiply-accumulate */
				/* acc0 +=  x[0] * y[srcBLen - 1] */
				acc0 += x0 * c0;

				/* acc1 +=  x[1] * y[srcBLen - 1] */
				acc1 += x1 * c0;

				/* acc2 +=  x[2] * y[srcBLen - 1] */
				acc2 += x2 * c0;

				/* acc3 +=  x[3] * y[srcBLen - 1] */
				acc3 += x3 * c0;

				/* Read y[srcBLen - 2] sample */
				c0 = *(py--);

				/* Read x[4] sample */
				x0 = *(px + 1u);

				/* Perform the multiply-accumulate */
				/* acc0 +=  x[1] * y[srcBLen - 2] */
				acc0 += x1 * c0;
				/* acc1 +=  x[2] * y[srcBLen - 2] */
				acc1 += x2 * c0;
				/* acc2 +=  x[3] * y[srcBLen - 2] */
				acc2 += x3 * c0;
				/* acc3 +=  x[4] * y[srcBLen - 2] */
				acc3 += x0 * c0;

				/* Read y[srcBLen - 3] sample */
				c0 = *(py--);

				/* Read x[5] sample */
				x1 = *(px + 2u);

				/* Perform the multiply-accumulates */
				/* acc0 +=  x[2] * y[srcBLen - 3] */
				acc0 += x2 * c0;
				/* acc1 +=  x[3] * y[srcBLen - 2] */
				acc1 += x3 * c0;
				/* acc2 +=  x[4] * y[srcBLen - 2] */
				acc2 += x0 * c0;
				/* acc3 +=  x[5] * y[srcBLen - 2] */
				acc3 += x1 * c0;

				/* Read y[srcBLen - 4] sample */
				c0 = *(py--);

				/* Read x[6] sample */
				x2 = *(px + 3u);
				px += 4u;

				/* Perform the multiply-accumulates */
				/* acc0 +=  x[3] * y[srcBLen - 4] */
				acc0 += x3 * c0;
				/* acc1 +=  x[4] * y[srcBLen - 4] */
				acc1 += x0 * c0;
				/* acc2 +=  x[5] * y[srcBLen - 4] */
				acc2 += x1 * c0;
				/* acc3 +=  x[6] * y[srcBLen - 4] */
				acc3 += x2 * c0;


			} while(--k);

			/* If the srcBLen is not a multiple of 4, compute any remaining MACs here.
			 ** No loop unrolling is used. */
			k = srcBLen % 0x4u;

			while(k > 0u) {
				/* Read y[srcBLen - 5] sample */
				c0 = *(py--);

				/* Read x[7] sample */
				x3 = *(px++);

				/* Perform the multiply-accumulates */
				/* acc0 +=  x[4] * y[srcBLen - 5] */
				acc0 += x0 * c0;
				/* acc1 +=  x[5] * y[srcBLen - 5] */
				acc1 += x1 * c0;
				/* acc2 +=  x[6] * y[srcBLen - 5] */
				acc2 += x2 * c0;
				/* acc3 +=  x[7] * y[srcBLen - 5] */
				acc3 += x3 * c0;

				/* Reuse the present samples for the next MAC */
				x0 = x1;
				x1 = x2;
				x2 = x3;

				/* Decrement the loop counter */
				k--;
			}

			/* Store the result in the accumulator in the destination buffer. */
			*pOut++ = acc0;
			*pOut++ = acc1;
			*pOut++ = acc2;
			*pOut++ = acc3;

			/* Increment the pointer pIn1 index, count by 4 */
			count += 4u;

			/* Update the inputA and inputB pointers for next MAC calculation */
			px = pIn1 + count;
			py = pSrc2;


			/* Decrement the loop counter */
			blkCnt--;
		}


		/* If the blockSize2 is not a multiple of 4, compute any remaining output samples here.
		 ** No loop unrolling is used. */
		blkCnt = blockSize2 % 0x4u;

		while(blkCnt > 0u) {
			/* Accumulator is made zero for every iteration */
			sum = 0.0f;

			/* Apply loop unrolling and compute 4 MACs simultaneously. */
			k = srcBLen >> 2u;

			/* First part of the processing with loop unrolling.  Compute 4 MACs at a time.
			 ** a second loop below computes MACs for the remaining 1 to 3 samples. */
			while(k > 0u) {
				/* Perform the multiply-accumulates */
				sum += *px++ * *py--;
				sum += *px++ * *py--;
				sum += *px++ * *py--;
				sum += *px++ * *py--;

				/* Decrement the loop counter */
				k--;
			}

			/* If the srcBLen is not a multiple of 4, compute any remaining MACs here.
			 ** No loop unrolling is used. */
			k = srcBLen % 0x4u;

			while(k > 0u) {
				/* Perform the multiply-accumulate */
				sum += *px++ * *py--;

				/* Decrement the loop counter */
				k--;
			}

			/* Store the result in the accumulator in the destination buffer. */
			*pOut++ = sum;

			/* Increment the MAC count */
			count++;

			/* Update the inputA and inputB pointers for next MAC calculation */
			px = pIn1 + count;
			py = pSrc2;

			/* Decrement the loop counter */
			blkCnt--;
		}
	} else {
		/* If the srcBLen is not a multiple of 4,
		 * the blockSize2 loop cannot be unrolled by 4 */
		blkCnt = blockSize2;

		while(blkCnt > 0u) {
			/* Accumulator is made zero for every iteration */
			sum = 0.0f;

			/* srcBLen number of MACS should be performed */
			k = srcBLen;

			while(k > 0u) {
				/* Perform the multiply-accumulate */
				sum += *px++ * *py--;

				/* Decrement the loop counter */
				k--;
			}

			/* Store the result in the accumulator in the destination buffer. */
			*pOut++ = sum;

			/* Increment the MAC count */
			count++;

			/* Update the inputA and inputB pointers for next MAC calculation */
			px = pIn1 + count;
			py = pSrc2;

			/* Decrement the loop counter */
			blkCnt--;
		}
	}


	/* --------------------------
	 * Initializations of stage3
	 * -------------------------*/

	/* sum += x[srcALen-srcBLen+1] * y[srcBLen-1] + x[srcALen-srcBLen+2] * y[srcBLen-2] +...+ x[srcALen-1] * y[1]
	 * sum += x[srcALen-srcBLen+2] * y[srcBLen-1] + x[srcALen-srcBLen+3] * y[srcBLen-2] +...+ x[srcALen-1] * y[2]
	 * ....
	 * sum +=  x[srcALen-2] * y[srcBLen-1] + x[srcALen-1] * y[srcBLen-2]
	 * sum +=  x[srcALen-1] * y[srcBLen-1]
	 */

	/* In this stage the MAC operations are decreased by 1 for every iteration.
	   The blockSize3 variable holds the number of MAC operations performed */

	/* Working pointer of inputA */
	pSrc1 = (pIn1 + srcALen) - (srcBLen - 1u);
	px = pSrc1;

	/* Working pointer of inputB */
	pSrc2 = pIn2 + (srcBLen - 1u);
	py = pSrc2;

	/* -------------------
	 * Stage3 process
	 * ------------------*/

	while(blockSize3 > 0u) {
		/* Accumulator is made zero for every iteration */
		sum = 0.0f;

		/* Apply loop unrolling and compute 4 MACs simultaneously. */
		k = blockSize3 >> 2u;

		/* First part of the processing with loop unrolling.  Compute 4 MACs at a time.
		 ** a second loop below computes MACs for the remaining 1 to 3 samples. */
		while(k > 0u) {
			/* sum += x[srcALen - srcBLen + 1] * y[srcBLen - 1] */
			sum += *px++ * *py--;

			/* sum += x[srcALen - srcBLen + 2] * y[srcBLen - 2] */
			sum += *px++ * *py--;

			/* sum += x[srcALen - srcBLen + 3] * y[srcBLen - 3] */
			sum += *px++ * *py--;

			/* sum += x[srcALen - srcBLen + 4] * y[srcBLen - 4] */
			sum += *px++ * *py--;

			/* Decrement the loop counter */
			k--;
		}

		/* If the blockSize3 is not a multiple of 4, compute any remaining MACs here.
		 ** No loop unrolling is used. */
		k = blockSize3 % 0x4u;

		while(k > 0u) {
			/* Perform the multiply-accumulates */
			/* sum +=  x[srcALen-1] * y[srcBLen-1] */
			sum += *px++ * *py--;

			/* Decrement the loop counter */
			k--;
		}

		/* Store the result in the accumulator in the destination buffer. */
		*pOut++ = sum;

		/* Update the inputA and inputB pointers for next MAC calculation */
		px = ++pSrc1;
		py = pSrc2;

		/* Decrement the loop counter */
		blockSize3--;
	}

#else

	/* Run the below code for Cortex-M0 */

	float32_t* pIn1 = pSrcA;                       /* inputA pointer */
	float32_t* pIn2 = pSrcB;                       /* inputB pointer */
	float32_t sum;                                 /* Accumulator */
	uint32_t i, j;                                 /* loop counters */

	/* Loop to calculate convolution for output length number of times */
	for(i = 0u; i < ((srcALen + srcBLen) - 1u); i++) {
		/* Initialize sum with zero to carry out MAC operations */
		sum = 0.0f;

		/* Loop to perform MAC operations according to convolution equation */
		for(j = 0u; j <= i; j++) {
			/* Check the array limitations */
			if((((i - j) < srcBLen) && (j < srcALen))) {
				/* z[i] += x[i-j] * y[j] */
				sum += pIn1[j] * pIn2[i - j];
			}
		}

		/* Store the output in the destination buffer */
		pDst[i] = sum;
	}

#endif /*   #ifndef ARM_MATH_CM0        */

}

/**
 * @} end of Conv group
 */
